Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.

The emergence of blockchain technology makes it possible to address disparate distributed system security concerns in formerly ridiculous practices. A key factor of this ability is the decentralization of the symmetrically distributed ledgers of blockchain. Such decentralization has replaced several security functionalities of centralized authority with the use of cryptographic systems. That is, public or asymmetric cryptography is the key part of what makes blockchain technology possible. Recently, the blockchain experience introduces the chance for the healthcare field to implement these knowhows in their electronic records. This adoption supports retaining and sharing the symmetrical patient records with the appropriate alliance of hospitals and healthcare providers in a secure decentralized system, using asymmetric cryptography like hashing, digitally signed transactions, and public key infrastructure. These include specialized applications for drug tracking, applications for observing patients, or Electronic Health Records (EHR). Therefore, it is essential to notice that the principled awareness of the healthcare professionals is the leading point of the right perception ethics. In this work, we provide a thorough review of the issues and applications of utilizing blockchain in the healthcare and medical fields emphasizing the particular challenges and aspects. The study adopted a systematic review of secondary literature in answering the research question. Specifically, this paper aims to investigate how blockchain technology can be applied to improve the overall performance of the healthcare sector and to explore the various challenges and concerns of the application of blockchain in the healthcare system.

There is a significant need to give careful consideration to Capability Maturity Model Integration (CMMI) Level 2 specific practices—SP 1.3 “manage requirements changes,” and SP 1.4 “maintain bidirectional traceability of requirements,” especially in the context of small- and medium-sized software development organizations in Saudi Arabia, in order to assist such organizations in getting one step closer to achieving CMMI Level 2 certification. The objective of this research is to implement CMMI Level 2 specific practices—SP 1.3 and SP 1.4. In this paper, a workflow model for each specific practice has been developed. In addition, initial evaluation of the models has been discussed. It is necessary to highlight that this paper contributes not only to the implementation of SP 1.3 and SP 1.4 of Requirements management process area in the context of small- and medium-sized software development organizations but also to the body of empirical studies in various context. Data has been collected by exploring published research articles and high-level software process descriptions. Moreover, previous research works that dealt with the implementation of CMMI Level 2 process areas have been reviewed. Furthermore, research articles that provide guidance to software development organizations for implementing process areas of CMMI Level 2 in their environments have been considered. After careful analysis of the collected data, we have proposed the models for two specific practices of CMMI level 2, i.e., managing requirements change and maintaining bidirectional traceability of requirements. Each model is divided into core stages, and different activities associated with each stage are clearly indicated. Initial evaluation of the proposed models was also conducted using the expert review process. Based on the initial evaluation, we are confident that our proposed models are clear and easy to learn, follow, and use. Moreover, our models are applicable to small- and medium-sized software development organizations in Saudi Arabia. The proposed models can also assist such organizations in implementing these two specific practices. For further evaluation, we need to perform multiple case studies in an industrial setting to test the proposed models.